> For the complete documentation index, see [llms.txt](https://picsellia.gitbook.io/picsellia/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://picsellia.gitbook.io/picsellia/hyperparameter-tuning/overview.md).

# Overview

In machine learning, **hyperparameter optimization** or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.

The same kind of machine learning model can require different constraints, weights or learning rates to generalize different data patterns. These measures are called hyperparameters, and have to be tuned so that the model can optimally solve the machine learning problem. 

Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined loss function on given independent data. The objective function takes a tuple of hyperparameters and returns the associated loss.

It can be kind of scary, but Picsellia can help you find the best hyperparameters for your models [🔥](https://emojikeyboard.org/copy/Fire_Emoji_%F0%9F%94%A5?utm_source=extlink)

### TL; DR; 

Picsellia platform can help you find the **best hyperparameters** for your ML models, you just need to create a **scan** using our Python SDK, define the metric to optimize and the strategy to use ( grid, bayesien, etc. ) and launch it on your local machines using our CLI or on Cloud GPU if your have a premium Picsellia account. 

Jumpstart your hyperparameters tuning by following these steps :

{% content-ref url="/pages/-MZlOCtxS4fnjuHHvgl2" %}
[Install Picsell CLI](/picsellia/hyperparameter-tuning/install-picsell-cli.md)
{% endcontent-ref %}

{% content-ref url="/pages/-MZCMWp1OGSz4ET7Vbt5" %}
[Config](/picsellia/hyperparameter-tuning/config.md)
{% endcontent-ref %}

{% content-ref url="/pages/-MZqJGvLbTONUdn4BE15" %}
[Launch your Hyperparameters tuning](/picsellia/hyperparameter-tuning/launch-your-hyperparameters-tuning.md)
{% endcontent-ref %}

### What's a Scan ? 

Picsellia platform allows you to create **Scans**, it refers to a set of runs designed to test hyperparameters and find the best ones for your ML models.  

Basically you need  to set all the hyperparameters that you want to tune and the parameters to optimize, for example  the `evaluation loss`

The platform will then generate all the exploration runs to launch and will act as an Oracle to send you the newt training after yours is over. 

**Benefits:**

* **Distribute your hyperparameters search** accross all your organisation laptop or servers 
* Benefits from Optuna Bayesien Search and early stopping in order to **insure minimum time to converge to optimum**
* Control all your machines from Picsellia Platform 

### Focus on code

You can **pass the script to be run,** your requirements, the parameters to explore, we do the rest [🚀](https://emojikeyboard.org/copy/Rocket_Emoji_%F0%9F%9A%80?utm_source=extlink)\
Once everything is set up, you can let your machines and Picsellia do the rest,

### Leverage all your company laptops or server to speed up the process 

Creating a machine swarm has never been so easy, just install our CLI on all your machines, then all the available runs will be send to all the available machines, insuring you to exploit all the processing power you have to converge the fastest way possible !